论文信息 - Building Medium-Vocabulary Isolated-Word Lithuanian HMM Speech Recognition System

Building Medium-Vocabulary Isolated-Word Lithuanian HMM Speech Recognition System

In this paper, the opening work on the development of a Lithuanian HMM speech recognition system is described. The triphone single-Gaussian HMM speech recognition system based on Mel Frequency Cepstral Coefficients (MFCC) was developed using HTK toolkit. Hidden Markov model's parameters were estimated from phone-level hand-annotated Lithuanian speech corpus. The system was evaluated on a speaker-independent <750 distinct isolated-word recognition task. Though the speaker adaptation and language modeling techniques were not used, the system was performing at 20% word error rate.

Gailius Raskinis | Danute Raskiniene

[1] Antanas Lipeika,et al. Optimal Segmentation of Random Sequences , 2000, Informatica.

[2] Steve Young,et al. The HTK book , 1995 .

[3] Laimutis Telksnys,et al. Development of Isolated Word Speech Recognition System , 2002, Informatica.

[4] Eugene Charniak,et al. Statistical language learning , 1997 .

[5] Vytautas Rudzionis,et al. Phoneme recognition in fixed context using regularized discriminant analysis , 1999, EUROSPEECH.

[6] James H. Martin,et al. Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[7] Frederick Jelinek,et al. Statistical methods for speech recognition , 1997 .